High precision gamut mapping

ABSTRACT

Methods and systems for gamut mapping are disclosed. Pixels of an image or points of a look-up-table can be gamut mapped in a multi-step iterative process, by generating a coarse gamut hull and calculating a value of a distance metric for out-of-gamut pixels or LUT points, and subsequently generating a fine gamut hull in the neighborhood of the coarse gamut hull points closest to the out-of-gamut pixel or LUT points under consideration. The out-of-gamut pixel or LUT point is gamut mapped based on the smallest distance metric value calculated.

TECHNICAL FIELD

The present disclosure relates to digital image processing. Moreparticularly, it relates to high precision gamut mapping method.

BRIEF DESCRIPTION OF DRAWINGS

The accompanying drawings, which are incorporated into and constitute apart of this specification, illustrate one or more embodiments of thepresent disclosure and, together with the description of exampleembodiments, serve to explain the principles and implementations of thedisclosure.

FIG. 1 illustrates a specific gamut as grid and the mapping of colorsthat are outside this gamut towards points on the gamut surface usingtwo different exemplary gamut mapping algorithms, ‘LCLIP’ and ‘WmindE’(both gamut mapping algorithms are prior art).

FIG. 2 illustrates an example of mapping pixels.

FIG. 3 illustrates a method for gamut mapping pixels with coarse andfine steps.

FIG. 4 depicts an exemplary flowchart for the methods of the presentdisclosure

FIG. 5 depicts an exemplary embodiment of a target hardware forimplementation of an embodiment of the present disclosure.

SUMMARY

In a first aspect of the disclosure, a computer-implemented method isdescribed for gamut mapping from a gamut mapping color space to a targetcolor space, the method comprising: providing, by a computer, an imagein the gamut mapping color space, the image comprising a plurality ofpixels; determining for the plurality of pixels, by the computer,out-of-gamut (OOG) pixels, based on a gamut of a target device;generating, by the computer, a coarse gamut hull comprising IG points ina course grid having an m-bit resolution, wherein m is an integernumber, the course grid being defined in the target color space;converting, by the computer, the coarse gamut hull to the gamut mappingcolor space; calculating, by the computer, for at least one OOG pixel aplurality of values of a coarse distance metric between the at least oneOOG pixel and each of a plurality of coarse gamut hull points in thecoarse grid of IG points, wherein calculating the coarse distance metricvalues is carried out in the gamut mapping color space; among theplurality of coarse gamut hull points, calculating, by the computer, ucoarse gamut hull points having a smaller coarse distance metric valuethan other coarse gamut hull points of the plurality of coarse gamuthull points, wherein u is an integer number, e.g. determining the ucoarse gamut hull points corresponding to the u smallest coarse distancemetric values; for each of the u coarse gamut hull points, generating,by the computer, a fine gamut hull comprising IG points in aneighborhood of each of the u coarse gamut hull points, in a fine gridhaving a q bit resolution, wherein q is an integer number higher than m,the fine grid being a regular grid in the target color space;converting, by the computer, the fine gamut hull to the gamut mappingcolor space; calculating, by the computer, for the at least one OOGpixel a plurality of values of a fine distance metric between the atleast one OOG pixel and each of a plurality of fine gamut hull points inthe fine grid of IG points, wherein calculating the fine distance metricvalues is carried out in the gamut mapping color space; calculating, bythe computer, a smallest distance metric value among the calculated finedistance metric values; and gamut mapping, by the computer, the at leastone OOG pixel to an IG pixel based on the calculated smallest distancemetric value.

In a second aspect of the disclosure, a computer-implemented method isdescribed for generating a look-up-table (LUT) for gamut mapping imagesfrom a gamut mapping color space to a target color space, the methodcomprising: providing, by a computer, a LUT for a target device, the LUTcomprising a plurality of LUT points in the gamut mapping color space tobe mapped to the target color space; determining for the plurality ofLUT points, by the computer, out-of-gamut (OOG) LUT points, based on agamut of the target device; generating, by the computer, a coarse gamuthull comprising IG points in a coarse grid having an m-bit resolution,wherein m is an integer number, the course grid being a regular griddefined in the target color space; converting, by the computer, thecoarse gamut hull to the gamut mapping color space; calculating, by thecomputer, for at least one OOG LUT point a plurality of values of acoarse distance metric between the at least one OOG LUT point and eachof a plurality of coarse gamut hull points in the coarse grid of IGpoints, wherein calculating the coarse distance metric values is carriedout in the gamut mapping color space; among the plurality of coarsegamut hull points, calculating, by the computer, u coarse gamut hullpoints having a smaller coarse distance metric value than other coarsegamut hull points of the plurality of coarse gamut hull points, whereinu is an integer number, e.g. determining the u coarse gamut hull pointscorresponding to the u smallest coarse distance metric values; for eachof the u coarse gamut hull points, generating, by the computer, a finegamut hull comprising IG points in a neighborhood of each of the ucoarse gamut hull points, in a fine grid having a q bit resolution,wherein q is an integer number higher than m, the fine grid being aregular grid defined in the target color space; converting, by thecomputer, the fine gamut hull to the gamut mapping color space;calculating, by the computer, for the at least one OOG LUT point aplurality of values of a fine distance metric between the at least oneOOG LUT point and a plurality of fine gamut hull points in the fine gridof IG points, wherein calculating the fine distance metric values iscarried out in the gamut mapping color space; calculating, by thecomputer, a smallest distance metric value among the calculated finedistance metric values; gamut mapping, by the computer, the at least oneOOG LUT point to an IG point based on the calculated smallest distancemetric value; and generating the LUT for gamut mapping by including theIG point as value of the at least one OOG LUT point.

In a third aspect of the invention, a non-transitory computer-readablestorage medium is described, having stored thereon computer-executableinstructions for executing any of the methods described in the presentdisclosure.

In a fourth aspect of the invention, an apparatus is described,configured to implement any of the method described in the presentdisclosure.

DETAILED DESCRIPTION

The present disclosure describes a method for hierarchical gamut mappingusing limited memory resources. The method is based on a coarse samplingstep to identify the region for optimal color mapping and subsequentlyproceeds with a finer sampling to identify the final mapping in amulti-step procedure.

The present disclosure describes methods that can applied to a varietyof applications involving gamut mapping, comprising image and videoprocessing, such as in the film and TV industry or traditional printing.For example, the methods of the present disclosure can apply toprinters, ranging from small home printers to large wide format printingmachines. In another example, the methods of the present disclosureapply to displays.

As understood by the person of ordinary skill in the art, gamut mappingrefers to the process of mapping colors that cannot otherwise bereproduced by a target device. For example, a device may have theability to display a range of colors. If the content, whether image orvideo, comprises colors outside the capability of the device, then thosecolors need to be mapped into the range that the device is capable ofdisplaying. Therefore, the colors of the content are mapped to the gamutof the individual device.

The range of colors, or gamut, can be displayed in different ways. FIG.1 illustrates the limits of a specific gamut as a grid (110). Somecolors are outside the gamut (105), and are mapped to the grid (110) inorder of being displayed by the device.

Classic gamut mapping algorithms can be defined as geometric operation.For example, 3b LCLIP preserves hue and lightness and can be implementedby calculating the intersection between the device gamut, represented astriangles, and a line from the out-of-gamut point to be mapped to thegray axis.

Other advanced gamut mapping algorithms, such asweighted-minimum-delta-E (WmindE), are not defined as geometricoperation but as a distance metric in perceptually correlated lightness,chroma and hue.

A problem with existing minimum delta E methods is how the target devicecolor volume is represented. Typically, these methods use a small numberof triangles to sample the volume, due to memory limitations. Thetypically low number of triangles is 17×17×2×6=3468. When determiningthe minimum distance for an out of gamut (OOG) pixel to the gamutboundary, the algorithm of these methods calculates the distance to eachtriangle-face, triangle-line and triangle-vertice according to thedistance metric. However, since gamut volumes tend to be non-linear inshape, this methodology results in colors less (or more) saturated thenthey could be as well as banding for smooth contours that span a vertex.Using non-linear interpolation (such as B-splines) that better fits thesurface of the color volume would be one possible solution for thisproblem, however there is no closed form solution to find the nearestpoint on a B-spline surface.

The present disclosure addresses the issues described above. Ideally,the minimum distance would be computed for every single possible valueof the gamut volume represented as a vertex on the color volumeboundary. However, the number computed values would be too large tostore for conventional computer systems. Considering that the outersurface of a color volume is a distorted version of the outer surface ofthe color space cube in RGB, it is apparent that the number of points torepresent every possible RGB output value in the gamut surface is sixtimes the area of one face. Each face has 2^(N)×2^(N) points, where N isthe bit depth of the interface. Thus, the number of points needed torepresent the 3D surface of an RGB gamut in a perceptual representationis 2^(N)×2^(N)×6, or 2^(2N)×6. For a 12 bit interface, this numberconstitutes over 100 million points, or about a hundred more than can besupported by typical memory footprints. To overcome this limitation themethods of the present disclosure first use a course sampling to findthe region containing the optimal point, followed by a second finesampling to refine the estimate to the exact point. This allowscalculating the optimal solution without a need for interpolation, andwith a reduced memory footprint.

Prior implementations of WmindE use either vertex, line and trianglebased gamut representations, lookup tables or multi-step iterativeapproaches, as described in Morovic, Ján, and Pei-Li Sun, “Non-IterativeMinimum ΔE Gamut Clipping”, Color and Imaging Conference, Vol. 2001, No.1, Society for Imaging Science and Technology, 2001, the disclosure ofwhich is incorporated herein by reference in its entirety.

Approaches that use a vertex, line and triangle based gamut hullrepresentation can suffer from loss of tonal resolution or detail,because large areas of out-of-gamut (OOG) colors are mapped to the samein gamut (IG) color. This process is illustrated in FIG. 2.

In FIG. 2, a line (205) represents the gamut boundary discretized totriangles. Colors outside the boundary (205), such as colors (210) and(215), are mapped onto the boundary. OOG colors a (210) and b (215) aremapped to different IG colors, (220) and (225), while OOG colors c (230)and d (235) are mapped to the same IG color (240). Therefore, anydistinction between colors (230) and (235) is lost when the colors aremapped to the new gamut. This discontinuity can result in bandingartifacts and loss of tonal details in smooth image areas.

Iterative approaches, on the other hand, are often slow and theirrun-time is not predictable because common implementations run until acertain convergence criterion is reached.

The present disclosure presents, instead, an n-step hierarchicalapproach that finishes within a predefined number of computing steps:Given a target color space (for example 8 bit RGB) all possible RGBvalues that lie on the gamut boundary are transformed to the gamutmapping color space, i.e. the input color space. To find the best gamutmatch for each pixel, first the value of a distance metric is calculatedbetween said pixel and the transferred RGB input points, wherein the RGBinput points are sampled according to a course grid. For example, in the8 bit case and with n=2 (two step hierarchical) a 5 bit resolutionresults in 6 planes of 32×32 points each. In the next step of themethod, the value of the distance metric is calculated for theneighborhood of each of the u points that resulted in the smallestdistance metric value in the coarse sampling. For example theneighborhood of a coarse gamut hull point may comprise the nearestneighbor IG points.

FIG. 3 illustrates an example for the color space sRGB, appearance modelIPT-PQ, and n=2. As known to the person of ordinary skill in the art,sRGB is a color space that lies close to the center of the RGB colorspace. IPT is a color appearance model, a mathematical model that seeksto describe the perceptual aspects of human color vision. The IPT colorappearance model is useful at providing a formulation for hue where aconstant hue value equals a constant perceived hue independent of thevalues of lightness and chroma. The cones of the human eye that aresensitive to color have differing sensitivities to each of the primaries(red, green, and blue). As a result, a mathematically lineardistribution of analyzed color is not necessarily the most accurate wayto represent what is actually seen. The IPT model consists of lineartransformations with additional nonlinear processing. In the IPT colorspace, I corresponds to the vertical axis of lightness (desaturatedblack to white) running through the center of the color space. Thehorizontal plane is defined by the P axis, which is the distribution ofred to green, and the T axis, which is the distribution of yellow toblue. The IPT model's non linearity can be exchanged with the PQnon-linear encoding, therefore obtaining the perceptually quantized (PQ)IPT model, or IPT-PQ model. Perceptual correction of a color space canbe carried out by analyzing the human perception of colors in the colorspace, for example through practical tests to detect the just noticeabledifference (JND) between color shades. IPT-PQ can be well-suited forgamut mapping implementations. In some embodiments, a modified versionof IPT-PQ is used as gamut mapping color space, i.e. input color space,and RGB as target color space. However, the methods described herein canbe used with any typical gamut mapping color space, for example Lab, IPTor CIECAM02. In some embodiments, the IPT-PQ appearance model can bemodified as described in PCT/US2015/051964, Encoding and DecodingPerceptually-Quantized Video Content, J. Froehlich et al., filed on 24Sep. 2015 the disclosure of which is incorporated herein by reference inits entirety. In FIG. 3, the full color space is illustrated on left(305), while a detail view of the search area for the second step isillustrated on the right (310).

In FIG. 3, points (315) represent the coarse sampling, that is, thefirst iteration; point (320) represents one result of the firstiteration; points (325) represent a fine sampling of the neighborhood ofpoint (320), that is, the second iteration; point (330) represents thefinal result for the weighted-minimum-delta-E distance metric on thesecond iteration grid. Point (330) is mapped in the fine sampling gamutfrom OOG pixel (340). A value of a distance metric (345) between OOGpoint (340) and resultant point (330) can be calculated.

In other words, coarse sampling utilizes points (315) with a certaindensity. In the immediate neighborhood of point (320) it is possible toidentify eight neighboring points (335). A finer sampling can be carriedout with points (325), whose density is higher than that of points(315). The mapping process from OOG pixel (340) yields a resultant point(330), for which a value of a distance metric (345) can be calculated.

For an n=2 (two step) implementation of the above method, the followingprecision (m) can be used: for 8 bit, a first iteration of 5 bit; for 10bit, a first iteration of 6 bit; for 12 bit, a first iteration of 7 bit.Other embodiments may comprise different values, for example n may behigher than 2 and different values of m may be used.

The methods of the present disclosure may comprise the following steps,with reference to FIGS. 3 and 4. In a first step (405), the code valuesof the gamut hull are calculated for a coarse resolution, for examplem-bit resolution. This coarse resolution corresponds to points (315). Insome embodiments, all the code values are calculated. In a next step(410), the discretized coarse gamut hull is converted to the gamutmapping color space.

Subsequently, for an out of gamut (OOG) color pixel, such as pixel(340), the OOG pixel is converted to the gamut mapping color space(415). In some embodiments, each OOG color pixel is converted to thegamut mapping color space. A plurality of values of a distance metric(345, 420) for the current OOG pixel is then calculated. A value of thedistance metric can be calculated from the current OOG pixel to thepoints of the coarse resolution gamut hull in the gamut mapping colorspace. In some embodiments, the value of the distance metric iscalculated to every point of the coarse resolution gamut hull in thegamut mapping color space.

In a next step (425), for the u nearest in gamut (IG) points of thecoarse resolution gamut hull, chosen based on the value of the distancemetric, code values are calculated for the gamut hull in theneighborhood of the current in gamut point. For example, the u nearestIG points can be points (335). In some embodiments, the code values canbe calculated for the points (325) that lay in the fine resolution gamuthull, delimited by the coarse resolution neighbors. In some embodiments,the code values are calculated for every point in the fine resolutiongamut hull in the neighborhood of the current IG point. These points inthe fine resolution gamut hull are then converted to the gamut mappingcolor space (430).

Fine distance metric values for the current OOG pixel are thencalculated (435). These distance metric values can be calculated fromthe current OOG pixel to the points of the fine resolution gamut hull inthe gamut mapping color space. In some embodiments, the fine distancemetric value is calculated to every point of the fine resolution gamuthull in the gamut mapping color space. The point in the higherresolution gamut hull with the smallest fine distance metric to the OOGpixel is picked (440) as the mapped IG point of the OOG pixel. The valueof the fine distance metric associated with the chosen point can also besaved for the subsequent calculations.

In other words, for an OOG pixel, a first coarse mapping is carried outwith a first mapped point, subsequently, a neighborhood of points aroundthis coarse mapped point is determined. A fine resolution gamut hull isdefined around the coarse mapped point and distance metric values arecalculated between these fine resolution gamut hull points and the OOGpixel. The point in the fine resolution gamut hull with the smallestdistance metric value is chosen as the fine mapped point of the OOGpixel. This method differs from prior multi-step approaches by regularlysampling the target RGB space and transferring this sampling to thegamut mapping space instead of optimizing the sampling for uniformity inthe mapping space.

The calculations above for the u nearest neighbors can be iterated for adifferent number u of neighbors. Again, the point in the fine resolutiongamut hull with the smallest distance metric value is chosen.Subsequently, the points with the smallest distance metric value fordifferent u neighbors can be compared and the point with the smallestdistance metric value for all u neighbors is picked as the gamut mappedvalue of the OOG pixel.

In other embodiments, the coarse resolution neighbors in the coarsegamut hull are calculated for each OOG pixel under consideration. Foreach of the coarse resolution neighbors, a distance metric value iscalculated, to the OOG pixel. Each of the coarse resolution neighbors isthen considered in turn. For each of these coarse resolution neighborpoints in the coarse gamut hull, a fine resolution grid of points isgenerated around it. The sampling of this grid is again determinedregularly in the target RGB space and transferred back to the gamutmapping space. The fine resolution grid is the fine gamut hull.Therefore, a fine gamut hull is calculated for each of the coarseresolution neighbors. The distance metric value is calculated betweeneach fine gamut hull point and the OOG pixel, for each of the coarsegamut hull points. In other word, the distance metric value for the finegamut hull points around each coarse gamut hull neighbor is calculated,the process being iterated for the fine gamut hull points around each ofthe coarse gamut hull neighbors. A mapped point for the OOG pixel isthen chosen, by finding the point in the fine gamut hull that has thesmallest distance metric value, i.e. the smallest distance to the OOGpixel according the distance metric.

In some embodiments, the algorithm can be directly used to gamut-mapimages. Alternatively, a 3D-LUT can be generated for performing gamutmapping. The 3D-LUT can then be subsequently applied to the image tofurther save computation time. For the 3D-LUT, additional processing canbe used to further refine the performance of the output LUT. Forexample, the output of the LUT can be processed with a 3D smootheningfilter to reduce the difference between adjacent points. This smoothsout sharp gradients that can cause issues for interpolation. Thesmoothing process can be applied to the whole LUT or just selectportions (such as out of gamut pixels, individual hues, saturations orspecial colors). In some embodiments, the LUT comprises OOG LUT points,which are used to map OOG pixels in an image. The methods describedherein can apply to the OOG LUT points of the LUT.

As described in the present disclosure, using a pre-quantized gamut hulland a multi-step hierarchical approach for gamut mapping presents afast, precise and simple to implement algorithm compared to iterativeapproaches, or vertex, line and triangle based gamut hullrepresentations.

Compared to iterative approaches as presented in Morovic et al. as citedabove, the time for gamut mapping each pixel is constant and thereforepredictable. Compared to approaches that use vertex, line and trianglebased gamut hull representations the implementation of the gamut mappingalgorithm does not introduce any additional loss of detail andtone-scale.

In some embodiments, the methods described in the present disclosure canbe used to create an LUT, or can be applied concurrently with an LUT, orcan be used as an alternative to a LUT to map out of gamut pixels. Insome embodiments, the input pixels of the algorithm are LUT points, e.g.coordinates of a 3D LUT. In other embodiments, with a direct approach,the input pixels are from the image or video frame to be mapped. The OOGpixels are selected for mapping with the IG pixels are not processed asno mapping is needed. In some embodiments, the gamut hull is initializedby generating a 2D plane for each color channel, for a total of 6planes. The gamut hull comprises all potential colors to map to,relative to the current OOG pixel under consideration for mapping. Theoptimized color to map to is decided by minimizing the distance metricvalue as described above. In some embodiments, the gamut hull isconverted to a linear form for easier calculation. A first index isgenerated for the coarse iteration, while the non-OOG pixels are copiedwithout processing and then OOG pixels are replaced according to themethod described above in the present disclosure. In some embodiments,the weighted minimum delta-E mapping algorithm is used, however in otherembodiments other mapping algorithm can be used. The coarse distancemetric values are calculated, from the current OOG pixel to the coarsegamut hull points. For example, in some embodiments the 32 nearestpoints in the coarse gamut hull can be used to calculate the coarsedistance metric value from the OOG pixel to each of the 32 nearestpoints. Subsequently, a neighborhood of fine gamut hull points isgenerated in the vicinity of each of the 32 nearest coarse points. Adistance metric value is calculated for each of the fine gamut hullpoints. The smallest distance metric value is selected for mapping theOOG pixel.

The methods described in the present disclosure quantize according to adevice dependent output space and therefore directly gain the best matchgamut mapped values, without the need for variable run-time iterativeapproaches. The method described herein improve the operation of thecomputer as the computational resources, such as memory, required forgamut mapping are significantly decreased compared to other previouslyknown methods.

In some embodiments, instead of a two-step approach with the calculationof a coarse resolution and a coarse distance metric value followed bythe calculation of a fine resolution and fine distance metric value, amultiple-step approach can be carried out. For example, each step hasincreased resolution, and a distance metric value is calculated for eachstep, therefore obtaining an increasingly accurate determination for theIG point most closely corresponding to the OOG point.

In some embodiments, a three-step approach can be used. For example, afirst distance metric value can be calculated, from the OOG point to aregion of the gamut hull with a first bit resolution. Once a minimumfirst distance is found, the process can proceed to the second step. Inthe second step, a second region of the gamut hull is determined, aroundthe point in the gamut hull with the minimum distance metric valuecalculated in the previous step; this second region has a higher bitresolution when compared to the region in the first step. A seconddistance metric value can be calculated, from the OOG point to thepoints in the second region of the gamut hull. In a third step, theprevious process is repeated by determining a third region on the gamuthull, around the point with the closest distance metric value calculatedin the second step. This third region has a higher bit resolution of thefirst and second regions. A third distance metric value can becalculated from the OOG point to the points in the third region of thegamut hull. This process can be extended to multiple steps, such as fouror more steps.

The multiple-step process can be advantageous when the method isexecuted in a device with limited memory and computational power. Thebit resolution for each step can be adjusted according to thecomputational resources available. A more powerful system may shortenthe processing time by reducing the number of steps and increasing theresolution of each step, while a less powerful system may increase thenumber of steps and decrease the bit resolution of the gamut hull regionin each step. For example, a first step may use a 6 bit resolution, asecond step may have a 12 bit resolution and a third step may have a 14bit resolution. Different bit resolution may be used according to thespecific application.

In some embodiments, a first determination of which IG point correspondsto the OOG pixel can be carried out with a triangle approach. Whiletriangle approaches, as discussed above in the present disclosure andillustrated in FIG. 2, have several disadvantages compared to themethods described herein, their application limited to the first steps,for example only to the first step, of the methods of the presentdisclosure may be useful in determining a first coarse IG pointcorresponding to the OOG pixel. For example, after a first applicationof the triangle approach, yielding a first coarse IG point for the OOGpixel, a grid can be determined around the coarse IG point, in the gamuthull, and a distance metric value can be calculated from the OOG pixelto the points in the gamut hull, as described above.

In some embodiments, the methods of the present disclosure are carriedout through the following exemplary steps. An input image is provided,for example in the ICtCp color space or equivalent color space. TheICtCp color space is described, for example, in Report ITU-R BT.2390-0,High dynamic range television for production and international programmeexchange, (2016), the disclosure of which is incorporated herein byreference in its entirety. In a subsequent step, ICtCp is converted toRGB based on the target display EOTF. EOTF stands for Electro-OpticalTransfer Function and it describes how to turn digital code words intovisible light. The conversion to RGB can be carried out through thefollowing steps: i. Apply a 3×3 matrix to convert ICtCp to UM′S′, whereprimes indicate nonlinearity; ii. Apply PQ curve to convert UM′S′ toLMS; iii. Apply a 3×3 matrix to convert LMS to RGB (using target displayRGB primaries); iv. Apply inverse EOTF to convert RGB to R′G′B′ (usingtarget display inverse EOTF, for example BT1886). LMS is a color spacerepresented by the response of the three types of cones of the humaneye, named after their responsivity at long, medium and shortwavelengths.

Following the conversion to RGB or R′G′B′, it is possible to calculate amask M from any pixels where RGB or R′G′B′ is <0 or >1. In a followingstep, it is possible to replace pixels in the mask M with the nearestin-gamut pixel as defined by the weighted-minimum-delta-E algorithm.

Alternately, the mask M can be computed after step iii, by comparing thelinear RGB values with the target display minimum and maximum luminance(where RGB<Tmin or>Tmax).

In some embodiments, the coarse gamut hull is shaped as a polyhedronformed by 12 faces originating from the vector addition of a RGB and awhite gamut hull. For example, the sRGB gamut hull consists of 6 planeswhile the RGB+White gamut hull consists of 12 planes.

In some embodiments, where the methods are applied to an LUT instead ofdirectly to an image, providing the LUT comprises applying three- orfour-dimensional filtering to the LUT prior to the providing. Forexample, filtering can be carried out by convolution or medianfiltering.

FIG. 5 is an exemplary embodiment of a target hardware (10) (e.g., acomputer system) for implementing the embodiments of FIGS. 2-4. Thistarget hardware comprises a processor (15), a memory bank (20), a localinterface bus (35) and one or more Input/Output devices (40). Theprocessor may execute one or more instructions related to theimplementation of FIGS. 2-4, and as provided by the Operating System(25) based on some executable program (30) stored in the memory (20).These instructions are carried to the processor (15) via the localinterface (35) and as dictated by some data interface protocol specificto the local interface and the processor (15). It should be noted thatthe local interface (35) is a symbolic representation of severalelements such as controllers, buffers (caches), drivers, repeaters andreceivers that are generally directed at providing address, control,and/or data connections between multiple elements of a processor basedsystem. In some embodiments the processor (15) may be fitted with somelocal memory (cache) where it can store some of the instructions to beperformed for some added execution speed. Execution of the instructionsby the processor may require usage of some input/output device (40),such as inputting data from a file stored on a hard disk, inputtingcommands from a keyboard, inputting data and/or commands from atouchscreen, outputting data to a display, or outputting data to a USBflash drive. In some embodiments, the operating system (25) facilitatesthese tasks by being the central element to gathering the various dataand instructions required for the execution of the program and providethese to the microprocessor. In some embodiments the operating systemmay not exist, and all the tasks are under direct control of theprocessor (15), although the basic architecture of the target hardwaredevice (10) will remain the same as depicted in FIG. 5. In someembodiments a plurality of processors may be used in a parallelconfiguration for added execution speed. In such a case, the executableprogram may be specifically tailored to a parallel execution. Also, insome embodiments the processor (15) may execute part of theimplementation of FIGS. 2-4, and some other part may be implementedusing dedicated hardware/firmware placed at an Input/Output locationaccessible by the target hardware (10) via local interface (35). Thetarget hardware (10) may include a plurality of executable programs(30), wherein each may run independently or in combination with oneanother.

The methods and systems described in the present disclosure may beimplemented in hardware, software, firmware or any combination thereof.Features described as blocks, modules or components may be implementedtogether (e.g., in a logic device such as an integrated logic device) orseparately (e.g., as separate connected logic devices). The softwareportion of the methods of the present disclosure may comprise acomputer-readable medium which comprises instructions that, whenexecuted, perform, at least in part, the described methods. Thecomputer-readable medium may comprise, for example, a random accessmemory (RAM) and/or a read-only memory (ROM). The instructions may beexecuted by a processor (e.g., a digital signal processor (DSP), anapplication specific integrated circuit (ASIC), a field programmablelogic array (FPGA), a graphic processing unit (GPU) or a general purposeGPU).

A number of embodiments of the disclosure have been described.Nevertheless, it will be understood that various modifications may bemade without departing from the spirit and scope of the presentdisclosure. Accordingly, other embodiments are within the scope of thefollowing claims.

The examples set forth above are provided to those of ordinary skill inthe art as a complete disclosure and description of how to make and usethe embodiments of the disclosure, and are not intended to limit thescope of what the inventor/inventors regard as their disclosure.

Modifications of the above-described modes for carrying out the methodsand systems herein disclosed that are obvious to persons of skill in theart are intended to be within the scope of the following claims. Allpatents and publications mentioned in the specification are indicativeof the levels of skill of those skilled in the art to which thedisclosure pertains. All references cited in this disclosure areincorporated by reference to the same extent as if each reference hadbeen incorporated by reference in its entirety individually.

It is to be understood that the disclosure is not limited to particularmethods or systems, which can, of course, vary. It is also to beunderstood that the terminology used herein is for the purpose ofdescribing particular embodiments only, and is not intended to belimiting. As used in this specification and the appended claims, thesingular forms “a,” “an,” and “the” include plural referents unless thecontent clearly dictates otherwise. The term “plurality” includes two ormore referents unless the content clearly dictates otherwise. Unlessdefined otherwise, all technical and scientific terms used herein havethe same meaning as commonly understood by one of ordinary skill in theart to which the disclosure pertains.

Various aspects of the present invention may be appreciated from thefollowing enumerated example embodiments (EEEs).

EEE 1. A computer-implemented method comprising:

providing, by a computer, an image comprising a plurality of pixels;

determining for the plurality of pixels, by the computer, in gamut (IG)pixels and out of gamut (OOG) pixels, based on a gamut of a targetdevice for displaying the image;

generating, by the computer, a coarse gamut hull comprising a coarsegrid of IG points with m-bit resolution, wherein m is an integer number;

calculating, by the computer, a coarse distance metric for at least oneOOG pixel, the coarse distance metric being between the OOG pixel and aplurality of coarse gamut hull points in the coarse grid of IG points;

among the plurality of coarse gamut hull points, calculating, by thecomputer, u coarse gamut hull points having a shorter distance metric tothe OOG pixel than other coarse gamut hull points of the plurality ofcoarse gamut hull points, wherein u is an integer number;

for each of the u coarse gamut hull points, generating, by the computer,a fine gamut hull comprising a fine grid of IG points in a neighborhoodof each of the u coarse gamut hull points, the fine grid having a q bitresolution, wherein q is an integer number higher than m;

calculating, by the computer, a fine distance metric for the at leastone OOG pixel, the fine distance metric being between the OOG pixel anda plurality of fine gamut hull points in the fine grid of IG points;

calculating, by the computer, a shortest distance metric among thecalculated fine distance metrics; and

gamut mapping, by the computer, the at least one OOG pixel to a IG pixelbased on the calculated shortest distance metric.

EEE 2. The method of EEE 1, wherein the plurality of coarse gamut hullpoints comprises all coarse grid IG points.EEE 3. The method of EEE 1, wherein u=32.EEE 4. The method of EEE 1, wherein the plurality of fine gamut hullpoints comprises all fine grid IG points.EEE 5. The method of EEE 1, wherein the calculated fine distance metricscomprise all fine distance metrics for all u coarse gamut hull points.EEE 6. The method of EEE 1, wherein generating, by the computer, acoarse gamut hull comprises converting the coarse gamut hull to a gamutmapping color space.EEE 7. The method of EEE 6, wherein determining for the plurality ofpixels, by the computer, OOG pixels comprises converting the OOG pixelsto the gamut mapping color space.EEE 8. The method of EEE 7, wherein calculating, by the computer, acoarse distance metric is carried out in the gamut mapping color space.EEE 9. The method of EEE 8, wherein generating, by the computer, a finegamut hull comprises converting the fine gamut hull to the gamut mappingcolor space.EEE 10. The method of EEE 9, wherein calculating, by the computer, afine distance metric is carried out in the gamut mapping color space.EEE 11. The method of EEE 10, further comprising displaying the imagewithin the gamut of the target device, the displayed image comprisingthe at least one gamut mapped OOG pixel.EEE 12. The method of EEE 11, wherein the gamut mapping color space isIPT-PQ.EEE 13. The method of EEE 12, wherein gamut mapping is carried out witha WmindE mapping algorithm.EEE 14. The method of EEE 1, further comprising:

generating, by the computer, a finer gamut hull comprising a finer gridof IG points with p-bit resolution, wherein p is an integer numberhigher than q; and

calculating, by the computer, a finer distance metric for the at leastone OOG pixel, the finer distance metric being between the OOG pixel anda plurality of finer gamut hull points in the finer grid of IG points,

wherein the calculating, by the computer, the shortest distance metricis among the calculated finer distance metrics.

EEE 15. The method of EEE 14, further comprising:

generating, by the computer, additional gamut hulls of IG points havingincreasing bit resolutions; and

calculating, by the computer, additional distance metrics for the atleast one OOG pixel between the OOG pixel and the additional gamuthulls,

wherein the calculating, by the computer, the shortest distance metricis among the calculated additional distance metrics.

EEE 16. A computer-implemented method comprising:

providing, by a computer, an image comprising a plurality of pixels;

providing, by a computer, a look-up-table (LUT) for a target device fordisplaying the image, the LUT comprising a plurality of LUT vertices tobe mapped;

determining for the plurality of LUT vertices, by the computer, in gamut(IG) LUT vertices and out of gamut (OOG) LUT vertices, based on a gamutof a target device for displaying the image;

generating, by the computer, a coarse gamut hull comprising a coarsegrid of IG points with m-bit resolution, wherein m is an integer number;

calculating, by the computer, a coarse distance metric for at least oneOOG vertex, the coarse distance metric being between the OOG vertex anda plurality of coarse gamut hull points in the coarse grid of IG points;

among the plurality of coarse gamut hull points, calculating, by thecomputer, u coarse gamut hull points having a shorter distance metric tothe OOG vertex than other coarse gamut hull points of the plurality ofcoarse gamut hull points, wherein u is an integer number;

for each of the u coarse gamut hull points, generating, by the computer,a fine gamut hull comprising a fine grid of IG points around each of theu coarse gamut hull points, the fine grid having a q bit resolution,wherein q is an integer number higher than m;

calculating, by the computer, a fine distance metric for the at leastone OOG vertex, the fine distance metric being between the OOG vertexand a plurality of fine gamut hull points in the fine grid of IG points;

calculating, by the computer, a shortest distance metric among thecalculated fine distance metrics;

gamut mapping, by the computer, the at least one OOG vertex to a IGvertex based on the calculated shortest distance metric;

gamut mapping, by the computer, at least one OOG pixel of the imagebased on the at least one gamut mapped OOG vertex of the LUT; and

displaying, by the computer, the image within the gamut of the targetdevice, the displayed image comprising the at least one gamut mapped OOGpixel.

EEE 17. The method of EEE 16, wherein the plurality of coarse gamut hullpoints comprises all coarse grid IG points.EEE 18. The method of EEE 16, wherein u=32.EEE 19. The method of EEE 16, wherein the plurality of fine gamut hullpoints comprises all fine grid IG points.EEE 20. The method of EEE 16, wherein the calculated fine distancemetrics comprise all fine distance metrics for all u coarse gamut hullpoints.EEE 21. The method of EEE 1, where positions of the plurality of coarseand fine gamut hull points are determined by:

sampling a regular grid of points on six surfaces of a cube-shaped gamutin a RGB target color space; and

transferring values of the sampled points to a gamut mapping space.

EEE 22. The method of EEE 21, wherein the coarse gamut hull is shaped asa polyhedron formed by 12 faces originating from a RGB and a white gamuthull.EEE 23. The method of EEE 16, wherein providing the LUT comprisesapplying three- or four-dimensional filtering to the LUT prior to theproviding.EEE 24. The method of EEE 23, wherein the filtering comprisesconvolution or median filtering.

1. A computer-implemented method for gamut mapping from a gamut mappingcolor space to a target color space, the method comprising: providing,by a computer, an image in the gamut mapping color space, the imagecomprising a plurality of pixels; determining for the plurality ofpixels, by the computer, |_([PL1])out-of-gamut (OOG) pixels, based on agamut of a target device; generating, by the computer, a coarse gamuthull comprising IG points in a course grid having an m-bit resolution,wherein m is an integer number, the course grid being a regular griddefined in the target color space; converting, by the computer, thecoarse gamut hull to the gamut mapping color space; calculating, by thecomputer, for at least one OOG pixel a plurality of values of a coarsedistance metric between the at least one OOG pixel and each of aplurality of coarse gamut hull points in the coarse grid of IG points,wherein calculating the coarse distance metric values is carried out inthe gamut mapping color space; among the plurality of coarse gamut hullpoints, calculating, by the computer, u coarse gamut hull points havinga smaller coarse distance metric value than other coarse gamut hullpoints of the plurality of coarse gamut hull points, wherein u is aninteger number; for each of the u coarse gamut hull points, generating,by the computer, a fine gamut hull comprising IG points in aneighborhood of each of the u coarse gamut hull points, in a fine gridhaving a q bit resolution, wherein q is an integer number higher than m,the fine grid being a regular grid in the target color space;converting, by the computer, the fine gamut hull to the gamut mappingcolor space; calculating, by the computer, for the at least one OOGpixel a plurality of values of a fine distance metric between the atleast one OOG pixel and each of a plurality of fine gamut hull points inthe fine grid of IG points, wherein calculating the fine distance metricvalues is carried out in the gamut mapping color space; calculating, bythe computer, a smallest distance metric value among the calculated finedistance metric values; and gamut mapping, by the computer, the at leastone OOG pixel to an IG pixel based on the calculated smallest distancemetric value.
 2. The method of claim 1, wherein the plurality of coarsegamut hull points comprises all coarse grid IG points of a boundary ofthe gamut of the target device.
 3. The method of claim 1, wherein u=32.4. The method of claim 1, wherein the plurality of fine gamut hullpoints comprises all fine grid IG points of a boundary of the gamut ofthe target device.
 5. The method of claim 1, wherein the calculated finedistance metric values comprise all fine distance metric values for allu coarse gamut hull points.
 6. The method of claim 1, wherein the targetdevice is a device for displaying the image, the method optionallyfurther comprising displaying the image within the gamut of the targetdevice, the displayed image comprising the at least one gamut mapped OOGpixel.
 7. The method of claim 6, wherein the gamut mapping color spaceis IPT-PQ, and/or wherein gamut mapping is carried out with aweighted-minimum-delta-E mapping algorithm.
 8. The method of claim 1,further comprising: generating, by the computer, a finer gamut hullcomprising a finer grid of IG points with p-bit resolution, wherein p isan integer number higher than q; and calculating, by the computer, forthe at least one OOG pixel a plurality of values of a finer distancemetric between the at least one OOG pixel and a plurality of finer gamuthull points in the finer grid of IG points, wherein the calculating, bythe computer, the smallest distance metric value is among the calculatedfiner distance metric values.
 9. The method of claim 8, furthercomprising: generating, by the computer, additional gamut hulls of IGpoints having increasing bit resolutions; and calculating, by thecomputer, additional distance metric values between the at least one OOGpixel and the additional gamut hulls, wherein the calculating, by thecomputer, the smallest distance metric value is among the calculatedadditional distance metrics.
 10. A computer-implemented method forgenerating a look-up-table (LUT) for gamut mapping images from a gamutmapping color space to a target color space, the method comprising:providing, by a computer, a LUT for a target device, the LUT comprisinga plurality of LUT points in the gamut mapping color space to be mappedto the target color space; determining for the plurality of LUT points,by the computer, |_([PL2])out-of-gamut (OOG) LUT points, based on agamut of the target device; generating, by the computer, a coarse gamuthull comprising IG points in a course grid having an m-bit resolution,wherein m is an integer number, the course grid being a regular griddefined in the target color space; converting, by the computer, thecoarse gamut hull to the gamut mapping color space; calculating, by thecomputer, for at least one OOG LUT point a plurality of values of acoarse distance metric between the at least one OOG LUT point and eachof a plurality of coarse gamut hull points in the coarse grid of IGpoints, wherein calculating the coarse distance metric values is carriedout in the gamut mapping color space; among the plurality of coarsegamut hull points, calculating, by the computer, u coarse gamut hullpoints having a smaller coarse distance metric value than other coarsegamut hull points of the plurality of coarse gamut hull points, whereinu is an integer number; for each of the u coarse gamut hull points,generating, by the computer, a fine gamut hull comprising IG points in aneighborhood of each of the u coarse gamut hull points, in a fine gridhaving a q bit resolution, wherein q is an integer number higher than m,the fine grid being a regular grid defined in the target color space;converting, by the computer, the fine gamut hull to the gamut mappingcolor space; calculating, by the computer, for the at least one OOG LUTpoint a plurality of fine distance metric values between the at leastone OOG LUT point and a plurality of fine gamut hull points in the finegrid of IG points, wherein calculating the fine distance metric valuesis carried out in the gamut mapping color space; calculating, by thecomputer, a smallest distance metric value among the calculated finedistance metric values; gamut mapping, by the computer, the at least oneOOG LUT point to an IG point based on the calculated smallest distancemetric value; and generating the look-up-table (LUT) for gamut mappingby including the IG point as value of the at least one OOG LUT point.11. The method of claim 10, further comprising: providing, by acomputer, an image comprising a plurality of pixels; and gamut mapping,by the computer, at least one OOG pixel of the image based on thegenerated LUT, wherein optionally: the target device is a device fordisplaying the image and the method further comprises displaying, by thecomputer, the image within the gamut of the target device, the displayedimage comprising the at least one gamut mapped OOG pixel.
 12. The methodof claim 10, where positions of the plurality of coarse and fine gamuthull points are determined by: sampling a regular grid of points on sixsurfaces of a cube-shaped gamut in a RGB target color space; andtransferring values of the sampled points to a gamut mapping space. 13.The method of claim 12, wherein the coarse gamut hull is shaped as apolyhedron formed by 12 faces originating from a RGB and a white gamuthull.
 14. The method of claim 10, wherein providing the LUT comprisesapplying three- or four-dimensional filtering to the LUT prior to theproviding, wherein optionally the filtering comprises convolution ormedian filtering.
 15. A non-transitory computer-readable storage mediumhaving stored thereon computer-executable instructions for executing amethod in accordance with claim
 1. 16. Apparatus configured to implementclaim 1 with one or more processors.